PRATIK PRIYANSHU

A research-grade, trust-aware Retrieval-Augmented Generation (RAG) system that integrates domain gating, hybrid retrieval, evidence clustering, contradiction detection, and confidence calibration to minimize hallucinations in high-stakes environments.

A research-grade framework for fair, controlled comparison of classical graph neural networks, variational quantum circuits, and hybrid classical–quantum architectures for molecular property prediction. Rather than assuming quantum advantage, the goal is to isolate architectural effects under consistent data preprocessing, batching, training, and evaluation protocols.

A lightweight inference engine designed for deploying deep learning models on edge devices with strict latency and memory constraints, using model compression and hardware-aware optimization.

Built an end-to-end privacy-preserving ML system enabling secure inference on sensitive healthcare data without exposing plaintext inputs to the server. The model never sees raw patient features — only encrypted vectors.

A modular ADAS perception and safety stack integrating camera, LiDAR, and radar fusion with interaction-aware prediction, explainable AI, safety diagnostics, and scenario validation, built to mirror German OEM architecture principles.